AIMC Topic: Neural Networks, Computer

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Convolutional Neural Networks for Pediatric Refractory Epilepsy Classification Using Resting-State Functional Magnetic Resonance Imaging.

World neurosurgery
OBJECTIVE: This study aims to evaluate the performance of convolutional neural networks (CNNs) trained with resting-state functional magnetic resonance imaging (rfMRI) latency data in the classification of patients with pediatric epilepsy from health...

Multi-scale Attention Convolutional Neural Network for time series classification.

Neural networks : the official journal of the International Neural Network Society
With the rapid increase of data availability, time series classification (TSC) has arisen in a wide range of fields and drawn great attention of researchers. Recently, hundreds of TSC approaches have been developed, which can be classified into two c...

Hardware Failure Prediction on Imbalanced Times Series Data : Generation of Artificial Data Using Gaussian Process and Applying LSTMFCN to Predict Broken Hardware.

Journal of digital imaging
Magnetic resonance imaging (MRI) systems and their continuous, failure-free operation is crucial for high-quality diagnostics and seamless workflows. One important hardware component is coils as they detect the magnetic signal. Before every MRI scan,...

An Automated Light Trap to Monitor Moths (Lepidoptera) Using Computer Vision-Based Tracking and Deep Learning.

Sensors (Basel, Switzerland)
Insect monitoring methods are typically very time-consuming and involve substantial investment in species identification following manual trapping in the field. Insect traps are often only serviced weekly, resulting in low temporal resolution of the ...

Shoulder kinematics plus contextual target information enable control of multiple distal joints of a simulated prosthetic arm and hand.

Journal of neuroengineering and rehabilitation
BACKGROUND: Prosthetic restoration of reach and grasp function after a trans-humeral amputation requires control of multiple distal degrees of freedom in elbow, wrist and fingers. However, such a high level of amputation reduces the amount of availab...

MATHLA: a robust framework for HLA-peptide binding prediction integrating bidirectional LSTM and multiple head attention mechanism.

BMC bioinformatics
BACKGROUND: Accurate prediction of binding between class I human leukocyte antigen (HLA) and neoepitope is critical for target identification within personalized T-cell based immunotherapy. Many recent prediction tools developed upon the deep learnin...

Prediction of implementing ISO 14031 guidelines using a multilayer perceptron neural network approach.

PloS one
The purpose of this study was to model the link between the implementation of ISO 14031 and ISO 14001. This study connects ISO 14031's guidelines as independent variables to a dependent variable expressed by the ISO 14001 certification situation of i...

Recurrent disease progression networks for modelling risk trajectory of heart failure.

PloS one
MOTIVATION: Recurrent neural networks (RNN) are powerful frameworks to model medical time series records. Recent studies showed improved accuracy of predicting future medical events (e.g., readmission, mortality) by leveraging large amount of high-di...

Brain tumor detection and multi-classification using advanced deep learning techniques.

Microscopy research and technique
A brain tumor is an uncontrolled development of brain cells in brain cancer if not detected at an early stage. Early brain tumor diagnosis plays a crucial role in treatment planning and patients' survival rate. There are distinct forms, properties, a...

Artificial neural networks combined multi-wavelength transmission spectrum feature extraction for sensitive identification of waterborne bacteria.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Present research is focused on the rapid and accurate identification of bacterial species based on artificial neural networks combined with spectral data processing technology. The spectra of different bacterial species in the logarithmic growth phas...